When applied to a likelihood [1]}, [2]}, the Dantzig selector estimate is obtained by minimizing \(\Vert \beta \Vert _1\)  subject to the infinite norm of the score function bounded by some threshold parameter \(\lambda \) . In the spatial point process setting, we propose an adaptive version of the Dantzig selector estimate as the solution of the problem
\(\min \Vert {\Lambda }_n \beta \Vert _1 \mbox{ subject to } {(\mu _n)}^{-1} \Big \Vert {\Lambda }_n^{-1} \mathbf {U}_n( {\beta }) \Big \Vert _\infty \le 1,\) 
